# Analyze calibration data
#Pulls data from .data files (written by demography-reset.R)
#Loads data into dataframes and generates plots
# Libraries ----------
rm(list=ls())
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2)
library(network)
## network: Classes for Relational Data
## Version 1.16.1 created on 2020-10-06.
## copyright (c) 2005, Carter T. Butts, University of California-Irvine
## Mark S. Handcock, University of California -- Los Angeles
## David R. Hunter, Penn State University
## Martina Morris, University of Washington
## Skye Bender-deMoll, University of Washington
## For citation information, type citation("network").
## Type help("network-package") to get started.
library(networkDynamic)
##
## networkDynamic: version 0.10.0, created on 2019-04-04
## Copyright (c) 2019, Carter T. Butts, University of California -- Irvine
## Ayn Leslie-Cook, University of Washington
## Pavel N. Krivitsky, University of Wollongong
## Skye Bender-deMoll, University of Washington
## with contributions from
## Zack Almquist, University of California -- Irvine
## David R. Hunter, Penn State University
## Li Wang
## Kirk Li, University of Washington
## Steven M. Goodreau, University of Washington
## Jeffrey Horner
## Martina Morris, University of Washington
## Based on "statnet" project software (statnet.org).
## For license and citation information see statnet.org/attribution
## or type citation("networkDynamic").
new_plots <- TRUE
old_plots <- TRUE
## Add names of data directories here
#Directory name format is {date}_full_run
bc_navigation_root <- '/project2/khanna7/bryanb/bc-navigation/dec9_navlength/bc-navigation/'
date<-"20:13:23_2021-03-22" #pre social nav update
#date <- '14:55:41_2021-03-12'
full_run_name <- paste0(date, '_full_run)/')
# Read data and set meta-parameters ----------
#setwd('/project2/khanna7/bryanb/bc-navigation/nov*/bc*')
getwd()
## [1] "/project2/khanna7/bryanb/bc-navigation/dec9_navlength/bc-navigation/data-analysis"
N <- 5000 #number of agents
n.instances <- 30 #number of runs
run_length=360 #number of time steps in run
control_list <- as.list(1:n.instances)
intervention_list <- as.list(1:n.instances)
noSocial_intervention_list <- as.list(1:n.instances)
control_dt_list <- as.list(1:n.instances)
intervention_dt_list <- as.list(1:n.instances)
noSocial_intervention_dt_list <- as.list(1:n.instances)
control_sc_list <- as.list(1:n.instances)
intervention_sc_list <- as.list(1:n.instances)
noSocial_intervention_sc_list <- as.list(1:n.instances)
dt_columns <- c(
#see `write.table` in https://github.com/khanna7/bc-navigation/blob/master/demography-reset.R for col names
"time", #TODO set to "time-step"
"nintros", #deaths
"number.of.positive.bc.agents",
"number.of.hpos.agents",
"number.of.hneg.agents",
"number.of.diagnosed.cases",
"number.of.diagnostic.referrals",
"number.of.screening.referrals",
"number.of.screen.completed",
"number.of.dt.completed",
"number.of.symptomatic",
"number.of.navigated.agents",
"time.with.cancer",
"time.until.diagnosis",
"time.until.diagnosis.navigated",
"time.until.diagnosis.unnavigated",
"time.until.diagnosis.neigbor.navigated",
"number.of.diagnostic.referrals.at.t",
"number.of.screening.visits.at.t",#19
"number.of.ss0.diagnosed",#20
"number.of.ss1.diagnosed",#21
"number.of.ss2.diagnosed",#22
"number.of.ss3.diagnosed",#23
"number.of.ss0.diagnosed.navigated",#24
"number.of.ss1.diagnosed.navigated",#25
"number.of.ss2.diagnosed.navigated",#26
"number.of.ss3.diagnosed.navigated",#27
"number.of.ss0.diagnosed.unnavigated",#28
"number.of.ss1.diagnosed.unnavigated",#29
"number.of.ss2.diagnosed.unnavigated",#30
"number.of.ss3.diagnosed.unnavigated",#31
"number.of.ss0.diagnosed.neighbor_navigated", #32
"number.of.ss1.diagnosed.neighbor_navigated", #33
"number.of.ss2.diagnosed.neighbor_navigated", #34
"number.of.ss3.diagnosed.neighbor_navigated", #35
"number.of.bc.onsets", #36
"number.of.screening.referrals.at.t",#37
"number.of.expired.diagnostic.referrals.at.t",#38
"number.of.expired.screening.referrals.at.t" #39
)
diagnostic_event_columns <- c(
"time",
"agent",
"diagnostic_referral_length",
"expired",
"within_2_months",
"symptom_severity",
"navigated",
"screening_referral_length",
"total_care_length",
"instance",
"cancer_status"
)
screening_event_columns <- c(
"time",
"agent",
"screening_referral_length",
"expired",
"within_2_months",
"symptom_severity",
"navigated",
"instance",
"cancer_status"
)
#create lists
for (i in 1:n.instances){
control_list[[i]] <- read.table(paste0(bc_navigation_root, date, '_full_run/', date, '_control/data/', i,".data"))
}
for (i in 1:n.instances){
intervention_list[[i]] <- read.table(paste0(bc_navigation_root, date, '_full_run/', date,'_intervention/data/', i,".data"))
}
for (i in 1:n.instances){
noSocial_intervention_list[[i]] <- read.table(paste0(bc_navigation_root, date, '_full_run/', date, '_interventionNoSocial/data/', i,".data"))
}
#Diagnostic event data handling
for (i in 1:n.instances){
control_dt_list[[i]] <- read.table(paste0(bc_navigation_root, date, '_full_run/', date, '_control/diagnostic_event_logs/', i,"_diagnostic.events"))
}
for (i in 1:n.instances){
intervention_dt_list[[i]] <- read.table(paste0(bc_navigation_root, date, '_full_run/', date,'_intervention/diagnostic_event_logs/', i,"_diagnostic.events"))
}
for (i in 1:n.instances){
noSocial_intervention_dt_list[[i]] <- read.table(paste0(bc_navigation_root, date, '_full_run/', date, '_interventionNoSocial/diagnostic_event_logs/', i,"_diagnostic.events"))
}
#Screening event data handling
for (i in 1:n.instances){
control_sc_list[[i]] <- read.table(paste0(bc_navigation_root, date, '_full_run/', date, '_control/diagnostic_event_logs/', i,"_screening.events"))
}
for (i in 1:n.instances){
intervention_sc_list[[i]] <- read.table(paste0(bc_navigation_root, date, '_full_run/', date,'_intervention/diagnostic_event_logs/', i,"_screening.events"))
}
for (i in 1:n.instances){
noSocial_intervention_sc_list[[i]] <- read.table(paste0(bc_navigation_root, date, '_full_run/', date, '_interventionNoSocial/diagnostic_event_logs/', i,"_screening.events"))
}
#check whether the listed data is of the right length. Returns the instances that are missing data.
which(unlist(lapply(control_list, nrow) != run_length)) #number of months of the simulation
## integer(0)
which(unlist(lapply(intervention_list, nrow) != run_length))
## integer(0)
which(unlist(lapply(noSocial_intervention_list, nrow) != run_length))
## integer(0)
control.df <- bind_rows(control_list[1:n.instances])
intervention.df <- bind_rows(intervention_list[1:n.instances])
noSocial_intervention.df <- bind_rows(noSocial_intervention_list[1:n.instances])
control_dt.df <- bind_rows(control_dt_list[1:n.instances])
intervention_dt.df <- bind_rows(intervention_dt_list[1:n.instances])
noSocial_intervention_dt.df <- bind_rows(noSocial_intervention_dt_list[1:n.instances])
colnames(control.df) <- dt_columns
colnames(intervention.df) <- dt_columns
colnames(noSocial_intervention.df) <- dt_columns
colnames(control_dt.df) <- diagnostic_event_columns
colnames(intervention_dt.df) <- diagnostic_event_columns
colnames(noSocial_intervention_dt.df) <- diagnostic_event_columns
#colnames(control_sc.df) <- screening_event_columns
#colnames(intervention_sc.df) <- screening_event_columns
#colnames(noSocial_intervention_sc.df) <- screening_event_columns
control.df$source <- rep(1:n.instances, each=run_length)
intervention.df$source <- rep(1:n.instances, each=run_length)
noSocial_intervention.df$source <- rep(1:n.instances, each=run_length)
# Compute incidence rate ----------
control.df <-
control.df %>%
mutate(number.of.bc.neg = N - number.of.positive.bc.agents)
intervention.df <-
intervention.df %>%
mutate(number.of.bc.neg = N - number.of.positive.bc.agents)
noSocial_intervention.df <-
noSocial_intervention.df %>%
mutate(number.of.bc.neg = N - number.of.positive.bc.agents)
# Compute means across variables at given time ----------
control.df_mean_at_time <-
control.df %>%
group_by(time) %>%
summarise(#m_navigation.length = mean(navigation.length),
m_time.until.diagnosis = median(time.until.diagnosis),
m_number.of.navigated.agents = mean(number.of.navigated.agents),
m_number.of.screening.visits.at.t = mean(number.of.screening.visits.at.t),
m_number.of.positive.bc.agents = mean(number.of.positive.bc.agents),
m_number.of.diagnostic.referrals.at.t = mean(number.of.diagnostic.referrals.at.t),
m_number.of.screening.referrals.at.t = mean(number.of.diagnostic.referrals.at.t),
m_number.of.screening.referrals = mean(number.of.screening.referrals),
m_number.of.dt.completed = mean(number.of.dt.completed),
m_number.of.screen.completed = mean(number.of.screen.completed),
m_number.of.hpos.agents = mean(number.of.hpos.agents),
m_number.of.hneg.agents = mean(number.of.hneg.agents),
m_number.of.diagnosed.cases = mean(number.of.diagnosed.cases),
m_number.of.positive.bc.agents = mean(number.of.positive.bc.agents),
m_nintros = mean(nintros),
m_early.diagnosed.ratio = mean((number.of.ss0.diagnosed+number.of.ss1.diagnosed)/number.of.diagnosed.cases),
m_late.diagnosed.ratio = mean((number.of.ss2.diagnosed+number.of.ss3.diagnosed)/number.of.diagnosed.cases),
m_number.of.expired.screening.referrals = mean(number.of.expired.screening.referrals.at.t),
m_number.of.expired.diagnostic.referrals = mean(number.of.expired.diagnostic.referrals.at.t)
)
intervention.df_mean_at_time <-
intervention.df %>%
group_by(time) %>%
summarise(#m_navigation.length = mean(navigation.length),
m_time.until.diagnosis = median(time.until.diagnosis),
m_number.of.navigated.agents = mean(number.of.navigated.agents),
m_number.of.screening.visits.at.t = mean(number.of.screening.visits.at.t),
m_number.of.positive.bc.agents = mean(number.of.positive.bc.agents),
m_number.of.diagnostic.referrals.at.t = mean(number.of.diagnostic.referrals.at.t),
m_number.of.screening.referrals = mean(number.of.screening.referrals),
m_number.of.dt.completed = mean(number.of.dt.completed),
m_number.of.screen.completed = mean(number.of.screen.completed),
m_number.of.hpos.agents = mean(number.of.hpos.agents),
m_number.of.hneg.agents = mean(number.of.hneg.agents),
m_number.of.diagnosed.cases = mean(number.of.diagnosed.cases),
m_number.of.positive.bc.agents = mean(number.of.positive.bc.agents),
m_nintros = mean(nintros),
m_early.diagnosed.ratio = mean((number.of.ss0.diagnosed+number.of.ss1.diagnosed)/number.of.diagnosed.cases),
m_late.diagnosed.ratio = mean((number.of.ss2.diagnosed+number.of.ss3.diagnosed)/number.of.diagnosed.cases)
)
noSocial_intervention.df_mean_at_time <-
noSocial_intervention.df %>%
group_by(time) %>%
summarise(#m_navigation.length = mean(navigation.length),
m_time.until.diagnosis = median(time.until.diagnosis),
m_number.of.navigated.agents = mean(number.of.navigated.agents),
m_number.of.screening.visits.at.t = mean(number.of.screening.visits.at.t),
m_number.of.positive.bc.agents = mean(number.of.positive.bc.agents),
m_number.of.diagnostic.referrals.at.t = mean(number.of.diagnostic.referrals.at.t),
m_number.of.screening.referrals = mean(number.of.screening.referrals),
m_number.of.dt.completed = mean(number.of.dt.completed),
m_number.of.screen.completed = mean(number.of.screen.completed),
m_number.of.hpos.agents = mean(number.of.hpos.agents),
m_number.of.hneg.agents = mean(number.of.hneg.agents),
m_number.of.diagnosed.cases = mean(number.of.diagnosed.cases),
m_number.of.positive.bc.agents = mean(number.of.positive.bc.agents),
m_nintros = mean(nintros),
m_early.diagnosed.ratio = mean((number.of.ss0.diagnosed+number.of.ss1.diagnosed)/number.of.diagnosed.cases),
m_late.diagnosed.ratio = mean((number.of.ss2.diagnosed+number.of.ss3.diagnosed)/number.of.diagnosed.cases)
)
##Not sure how this mean_at_time thing will work
# control_dt.df_mean_at_time <-
# control_dt.df %>%
# group_by(time) %>%
# summarise(
# m_late.diagnosed.ratio = mean((number.of.ss2.diagnosed+number.of.ss3.diagnosed)/number.of.diagnosed.cases)
# )
#
# intervention_dt.df_mean_at_time <-
# intervention_dt.df %>%
# group_by(time) %>%
# summarise(#m_navigation.length = mean(navigation.length),
# m_time.until.diagnosis = median(time.until.diagnosis),
# m_late.diagnosed.ratio = mean((number.of.ss2.diagnosed+number.of.ss3.diagnosed)/number.of.diagnosed.cases)
# )
#
# noSocial_intervention_dt.df_mean_at_time <-
# noSocial_intervention_dt.df %>%
# group_by(time) %>%
# summarise(#m_navigation.length = mean(navigation.length),
# m_time.until.diagnosis = median(time.until.diagnosis),
# )
# Plots ----------
#begin capturing output if you dont want to knit
#pdf(file='multiplot_out.pdf')
#Length of navigation (FAILED because demography-reset.R will break if I output nav.length because it is a vector of length 5000 and everything else is a single number)
#Analysis has been done manually collecting nav lengths from net.f and putting that through hist()
#Time from referral to testing (THIS WILL REQUIRE SUBSTANTIAL WORK: MODEL NEEDS TO RECORD TIME OF REFERRAL)
#Notes: color = "" must be inside aes(). Colors assigned in scale_color_manual use LIMITS not LABELS.
con<-"control"
ins<-"Institutional without social"
soc<-"Institutional with social"
#Examining expirations
ggplot(data=control.df_mean_at_time)+
geom_point(aes(x=time,y=m_number.of.expired.screening.referrals, color="screening expirys"))+
geom_point(aes(x=time,y=m_number.of.screening.referrals, color="total screening referrals"))+
geom_point(aes(x=time,y=m_number.of.screen.completed, color="screenings completed at t"))+
geom_point(aes(x=time,y=m_number.of.screening.referrals.at.t, color="number of screening referrals at t"))+
ylab("Number")+
scale_color_manual("Scenario:Control",
limits = c("screening expirys", "total screening referrals", "screenings completed at t","number of screening referrals at t"),
values = c('green','red','blue', "yellow"))
ggplot(data=control.df_mean_at_time)+
geom_point(aes(x=time,y=m_number.of.expired.diagnostic.referrals, color="diagnostic expirys"))+
geom_point(aes(x=time,y=m_number.of.diagnostic.referrals.at.t, color="total diagnostic referrals"))+
geom_point(aes(x=time,y=m_number.of.dt.completed, color="diagnostic completed at t"))+
ylab("Number")+
scale_color_manual("Scenario:Control",
limits = c("diagnostic expirys", "total diagnostic referrals", "diagnostic completed at t"),
values = c('green','red','blue'))
#without expirations
ggplot(data=control.df_mean_at_time)+
#geom_point(aes(x=time,y=m_number.of.expired.screening.referrals, color="screening expirys"))+
geom_point(aes(x=time,y=m_number.of.screening.referrals, color="total screening referrals"))+
geom_point(aes(x=time,y=m_number.of.screen.completed, color="screenings completed at t"))+
geom_point(aes(x=time,y=m_number.of.screening.referrals.at.t, color="number of screening referrals at t"))+
ylab("Number")+
scale_color_manual("Scenario:Control",
limits = c("screening expirys", "total screening referrals", "screenings completed at t","number of screening referrals at t"),
values = c('green','red','blue', "yellow"))
##New .dtestdata Plots
#Compare navigated vs non-navigated time of care
navigated_completions <- filter(intervention_dt.df, navigated == 1)
unnavigated_completions <- filter(intervention_dt.df, navigated == 0)
ggplot()+
geom_histogram(data=unnavigated_completions, aes(x=total_care_length), bins = 13)
ggplot()+
geom_histogram(data=navigated_completions, aes(x=total_care_length), bins = 13)
#Compare navigated vs. non-navigated diagnostic test referral to completion time
ggplot()+
geom_histogram(data=control_dt.df, aes(x=diagnostic_referral_length, color = con), fill = 'none')+
geom_histogram(data=noSocial_intervention_dt.df, aes(x=diagnostic_referral_length, color = ins), fill = 'none')+
geom_histogram(data=intervention_dt.df, aes(x=diagnostic_referral_length, color = soc), fill = 'none')+
scale_color_manual("Scenario", limits = c("control", "Institutional without social", "Institutional with social"),
values = c('green','red','blue'))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#By Percent of completions
control_tbl<-control_dt.df %>% group_by(diagnostic_referral_length)
noSocial_tbl<-noSocial_intervention_dt.df %>% group_by(diagnostic_referral_length)
social_tbl<-intervention_dt.df %>% group_by(diagnostic_referral_length)
control_pct <- table(group_indices(control_tbl))/length(control_tbl$time)
noSocial_pct <- table(group_indices(noSocial_tbl))/length(noSocial_tbl$time)
social_pct <- table(group_indices(social_tbl))/length(social_tbl$time)
#Cumulative sum
plot(cumsum(control_pct), type='b')
plot(cumsum(noSocial_pct), type='b')
plot(cumsum(social_pct), type='b')
#ggplot version of cumulative percent
ggplot()+
geom_line(data=data.frame(cumsum(control_pct)), aes(x=1:13,y=cumsum.control_pct.), color='green')+
ylab("Cumulative Percent")+
xlab("Diagnostic Referral Length")+
scale_x_continuous(breaks=1:13)+
theme_bw()+
ylim(c(0,1))
ggplot()+
geom_line(data=data.frame(cumsum(noSocial_pct)), aes(x=1:13,y=cumsum.noSocial_pct.), color='red')+
ylab("Cumulative Percent")+
xlab("Diagnostic Referral Length")+
scale_x_continuous(breaks=1:13)+
theme_bw()+
ylim(c(0,1))
ggplot()+
geom_line(data=data.frame(cumsum(social_pct)), aes(x=1:13,y=cumsum.social_pct.),color='blue')+
ylab("Cumulative Percent")+
xlab("Diagnostic Referral Length")+
scale_x_continuous(breaks=1:13)+
theme_bw()+
ylim(c(0,1))
ggplot()+
geom_line(data=data.frame(cumsum(social_pct)), aes(x=1:13,y=cumsum.social_pct.),color='blue')+
geom_line(data=data.frame(cumsum(noSocial_pct)), aes(x=1:13,y=cumsum.noSocial_pct.), color='red')+
geom_line(data=data.frame(cumsum(control_pct)), aes(x=1:13,y=cumsum.control_pct.), color='green')+
ylab("Cumulative Percent")+
xlab("Diagnostic Referral Length")+
scale_x_continuous(breaks=1:13)+
theme_bw()+
ylim(c(0,1))
#Histogram of completions by diagnostic referral length
ggplot()+
geom_histogram(data=control_dt.df, aes(x=diagnostic_referral_length, color = con), fill = 'none', bins=15, binwidth=1, boundary=-0.5)+
geom_histogram(data=noSocial_intervention_dt.df, aes(x=diagnostic_referral_length, color = ins), fill = 'none', bins=15, binwidth=1, boundary=-0.5)+
geom_histogram(data=intervention_dt.df, aes(x=diagnostic_referral_length, color = soc), fill = 'none', bins=15, binwidth=1, boundary=-0.5)+
ylab("Frequency")+
theme_bw()+
scale_x_continuous(name="Referral Length", breaks=-1:13, labels=-1:13)+ #No X-axis name or Labels for some reason??
scale_color_manual("Scenario", limits = c("control", "Institutional without social", "Institutional with social"),
values = c('green','red','blue'))
##Compare navigated vs. Non-navigated
#Length of diagnostic referral
ggplot()+
#geom_histogram(data=control_dt.df, aes(x=diagnostic_referral_length, color = con), fill = 'none', bins=15, binwidth=1, boundary=-0.5)+
geom_histogram(data=filter(intervention_dt.df, navigated==1), aes(x=diagnostic_referral_length, color = 'navigated'), fill = 'none', bins=15, binwidth=1, boundary=-0.5)+
geom_histogram(data=filter(intervention_dt.df, navigated==0), aes(x=diagnostic_referral_length, color = 'unnavigated'), fill = 'none', bins=15, binwidth=1, boundary=-0.5)+
ylab("Frequency")+
#ylim(0,31000)+
theme_bw()+
scale_x_continuous(name="Referral Length", breaks=-1:13, labels=-1:13)+ #No X-axis name or Labels for some reason??
scale_color_manual("Social + Institutional", limits = c("navigated", "unnavigated"),
values = c('blue', 'green'))
ggplot()+
#geom_histogram(data=control_dt.df, aes(x=diagnostic_referral_length, color = con), fill = 'none', bins=15, binwidth=1, boundary=-0.5)+
geom_histogram(data=filter(noSocial_intervention_dt.df, navigated==1), aes(x=diagnostic_referral_length, color = 'navigated'), fill = 'none', bins=15, binwidth=1, boundary=-0.5)+
geom_histogram(data=filter(noSocial_intervention_dt.df, navigated==0), aes(x=diagnostic_referral_length, color = 'unnavigated'), fill = 'none', bins=15, binwidth=1, boundary=-0.5)+
ylab("Frequency")+
ylim(0,31000)+
theme_bw()+
scale_x_continuous(name="Referral Length", breaks=-1:13, labels=-1:13)+ #No X-axis name or Labels for some reason??
scale_color_manual("Institutional Only", limits = c("navigated", "unnavigated"),
values = c('blue', 'green'))
#Plotting diagnostic length as by percent
#Symptom Severity at Logging
#SS social nav/nonnav
ggplot()+
#geom_histogram(data=control_dt.df, aes(x=diagnostic_referral_length, color = con), fill = 'none', bins=15, binwidth=1, boundary=-0.5)+
geom_histogram(data=filter(intervention_dt.df, navigated==1 ), aes(x=symptom_severity, color = 'navigated'), fill = 'none', bins=15, binwidth=1, boundary=-0.5)+
geom_histogram(data=filter(intervention_dt.df, navigated==0 ), aes(x=symptom_severity, color = 'unnavigated'), fill = 'none', bins=15, binwidth=1, boundary=-0.5)+
ylab("Frequency")+
theme_bw()+
scale_x_continuous(name="Symptom Severity at Logging", breaks=-1:13, labels=-1:13)+ #No X-axis name or Labels for some reason??
scale_color_manual("Social + Institutional", limits = c("navigated", "unnavigated"),
values = c('blue', 'green'))
#SS institutional nav/nonnav
ggplot()+
#geom_histogram(data=control_dt.df, aes(x=diagnostic_referral_length, color = con), fill = 'none', bins=15, binwidth=1, boundary=-0.5)+
geom_histogram(data=filter(noSocial_intervention_dt.df, navigated==1 ), aes(x=symptom_severity, color = 'navigated'), fill = 'none', bins=15, binwidth=1, boundary=-0.5)+
geom_histogram(data=filter(noSocial_intervention_dt.df, navigated==0 ), aes(x=symptom_severity, color = 'unnavigated'), fill = 'none', bins=15, binwidth=1, boundary=-0.5)+
ylab("Frequency")+
theme_bw()+
scale_x_continuous(name="Symptom Severity at Logging", breaks=-1:13, labels=-1:13)+ #No X-axis name or Labels for some reason??
scale_color_manual("Institutional Only", limits = c("navigated", "unnavigated"),
values = c('blue', 'green'))
#Plotting stage at logging by percent
n_total_referral_ends_control <- length(control_dt.df$time)
n_total_referral_ends_institutional <- length(noSocial_intervention_dt.df$time)
n_total_referral_ends_social <- length(intervention_dt.df$time)
nav_referral_ends_control <- filter(control_dt.df, navigated==1) #Should be 0 but isnt???
nav_referral_ends_institutional <- filter(noSocial_intervention_dt.df, navigated==1)
nav_referral_ends_social <- filter(intervention_dt.df, navigated==1)
unnav_referral_ends_control <- filter(control_dt.df, navigated==0)
unnav_referral_ends_institutional <- filter(noSocial_intervention_dt.df, navigated==0)
unnav_referral_ends_social <- filter(intervention_dt.df, navigated==0)
#SS Breakdowns
control_SS_nav <-table(nav_referral_ends_control$symptom_severity)
control_SS_unnav<-table(unnav_referral_ends_control$symptom_severity)
control_SS_total<-table(control_dt.df$symptom_severity)
institutional_SS_nav <-table(nav_referral_ends_institutional$symptom_severity)
institutional_SS_unnav<-table(unnav_referral_ends_institutional$symptom_severity)
institutional_SS_total<-table(noSocial_intervention_dt.df$symptom_severity)
social_SS_nav <-table(nav_referral_ends_social$symptom_severity)
social_SS_unnav<-table(unnav_referral_ends_social$symptom_severity)
social_SS_total<-table(intervention_dt.df$symptom_severity)
#SS Breakdown by percent
p_control_SS_nav<-control_SS_nav/sum(control_SS_nav)*100
p_control_SS_unnav<-control_SS_unnav/sum(control_SS_unnav)*100
p_control_SS_total<-control_SS_total/sum(control_SS_total)*100
p_institutional_SS_nav <-institutional_SS_nav/sum(institutional_SS_nav)*100
p_institutional_SS_unnav<-institutional_SS_unnav/sum(institutional_SS_unnav)*100
p_institutional_SS_total<-institutional_SS_total/sum(institutional_SS_total)*100
p_social_SS_nav <-social_SS_nav/sum(social_SS_nav)*100
p_social_SS_unnav<-social_SS_unnav/sum(social_SS_unnav)*100
p_social_SS_total<- social_SS_total/sum(social_SS_total)*100
#Plotting symptom severity percentage breakdown
ggplot()+
theme_bw()+
geom_bar(stat="identity",data=data.frame(p_control_SS_nav), aes(x=Var1,y=Freq), color="blue", fill='none')+
geom_bar(stat="identity",data=data.frame(p_control_SS_unnav), aes(x=Var1,y=Freq), color="green", fill='none')+
ylab("Percent")+
xlab("Symptom Severity Level/Cancer Stage")
ggplot()+
theme_bw()+
geom_bar(stat="identity",data=data.frame(p_institutional_SS_nav), aes(x=Var1,y=Freq), color="blue", fill='none')+
geom_bar(stat="identity",data=data.frame(p_institutional_SS_unnav), aes(x=Var1,y=Freq), color="green", fill='none')+
ylab("Percent")+
xlab("Symptom Severity Level/Cancer Stage")
ggplot()+
theme_bw()+
geom_bar(stat="identity",data=data.frame(p_social_SS_nav), aes(x=Var1,y=Freq), color="blue", fill='none')+
geom_bar(stat="identity",data=data.frame(p_social_SS_unnav), aes(x=Var1,y=Freq), color="green", fill='none')+
ylab("Percent")+
xlab("Symptom Severity Level/Cancer Stage")
#Output for symptom severity table
p_control_SS_nav
##
## 1 2 3
## 64.88483 24.46762 10.64754
p_control_SS_unnav
##
## 0 1 2 3
## 82.971915 10.607401 4.500661 1.920023
p_control_SS_total
##
## 0 1 2 3
## 80.720519 12.080189 5.042453 2.156840
p_institutional_SS_nav
##
## 0 1 2 3
## 87.589740 8.416610 2.788701 1.204950
p_institutional_SS_unnav
##
## 0 1 2 3
## 81.799500 11.516684 4.824246 1.859569
p_institutional_SS_total
##
## 0 1 2 3
## 83.448949 10.633575 4.244386 1.673090
p_social_SS_nav
##
## 0 1 2 3
## 93.1389304 4.6482233 1.5399131 0.6729331
p_social_SS_unnav
##
## 0 1 2 3
## 81.868406 11.557910 4.730637 1.843047
p_social_SS_total
##
## 0 1 2 3
## 87.039805 8.387450 3.266596 1.306148
#Difference:
p_control_SS_nav[1:4] - p_control_SS_unnav[1:4] #Doesn't work because the navigated array is shorter
##
## 1 2 3 <NA>
## -18.087082 13.860221 6.146884
p_institutional_SS_nav - p_institutional_SS_unnav
##
## 0 1 2 3
## 5.7902400 -3.1000748 -2.0355454 -0.6546198
p_social_SS_nav - p_social_SS_unnav
##
## 0 1 2 3
## 11.270525 -6.909687 -3.190724 -1.170114
#length(which(nav_referral_ends_control$navigated==1))
#length(filter(intervention_dt.df, navigated==1 & symptom_severity==0)$time)/length(filter(intervention_dt.df,navigated==1)$time)
#Managing data: Dividing event log into instances
#Cumulative percent (nav vs. unnav)
#control_tbl<-control_dt.df %>% group_by(diagnostic_referral_length)
#noSocial_tbl<-noSocial_intervention_dt.df %>% group_by(diagnostic_referral_length)
social_nav_tbl<-filter(intervention_dt.df, navigated==1) %>% group_by(diagnostic_referral_length)
social_unnav_tbl<-filter(intervention_dt.df, navigated==0) %>% group_by(diagnostic_referral_length)
#control_pct <- table(group_indices(control_tbl))/length(control_tbl$time)
#noSocial_pct <- table(group_indices(noSocial_tbl))/length(noSocial_tbl$time)
social_nav_pct <- table(group_indices(social_nav_tbl))/length(social_nav_tbl$time)
social_unnav_pct <- table(group_indices(social_unnav_tbl))/length(social_unnav_tbl$time)
#Cumulative sum
plot(cumsum(control_pct), type='b')
plot(cumsum(noSocial_pct), type='b')
plot(cumsum(social_pct), type='b')
#Social Intervention Nav vs Unnav
ggplot()+
geom_line(data=data.frame(cumsum(social_nav_pct)), aes(x=1:13,y=cumsum.social_nav_pct.),color='blue')+
geom_line(data=data.frame(cumsum(social_unnav_pct)), aes(x=1:13,y=cumsum.social_unnav_pct.),color='green')+
ylab("Cumulative Percent")+
xlab("Diagnostic Referral Length")+
scale_x_continuous(breaks=1:13)+
theme_bw()+
ylim(c(0,1))
##Old .data plots
#if(old_plots == TRUE){
#GOAL: Percent dt referrals completed/ total number of completions. i.e. "73% of completions were in month 1-2"
#NOTE: I think this is flawed. Based on the tiny y-values I infer that in the control case this is giving us ~36% return over 2 years.
#I'll have to look at the total number of screen completions and divide it by person-years. We want to see 1 completion/2 person-years?
ggplot()+
theme_bw()+
geom_line(data = control.df_mean_at_time, aes(x=time, y=m_number.of.screen.completed/m_number.of.screening.referrals,color=con))+
geom_line(data=control.df, alpha=0.1, aes(x=time, y=number.of.screen.completed/number.of.screening.referrals,color=con))+
geom_line(data = noSocial_intervention.df_mean_at_time, aes(x=time,y=m_number.of.screen.completed/m_number.of.screening.referrals,color=ins))+
geom_line(data=noSocial_intervention.df, alpha=0.1, aes(x=time, y=number.of.screen.completed/number.of.screening.referrals, color=ins))+
geom_line(data = intervention.df_mean_at_time, aes(x=time, y=m_number.of.screen.completed/m_number.of.screening.referrals, color=soc))+
geom_line(data=intervention.df, alpha=0.1, aes(x=time, y=number.of.screen.completed/number.of.screening.referrals, color=soc))+
scale_color_manual("Scenario", limits = c("control", "Institutional without social", "Institutional with social"),
values = c('green','red','blue')
)#+
#ylim(c(0,2))
#Screening completions/dt completions
#Is this useful? We seem to have huge spikes (100+ screen complete/dt complete)
ggplot()+
theme_bw()+
geom_line(data = control.df_mean_at_time, aes(x=time, y=m_number.of.screen.completed/m_number.of.dt.completed,color=con))+
geom_line(data=control.df, alpha=0.1, aes(x=time, y=number.of.screen.completed/number.of.dt.completed,color=con))+
geom_line(data = noSocial_intervention.df_mean_at_time, aes(x=time,y=m_number.of.screen.completed/m_number.of.dt.completed,color=ins))+
geom_line(data=noSocial_intervention.df, alpha=0.1, aes(x=time, y=number.of.screen.completed/number.of.dt.completed, color=ins))+
geom_line(data = intervention.df_mean_at_time, aes(x=time, y=m_number.of.screen.completed/m_number.of.dt.completed, color=soc))+
geom_line(data=intervention.df, alpha=0.1, aes(x=time, y=number.of.screen.completed/number.of.dt.completed, color=soc))+
scale_color_manual("Scenario", limits = c("control", "Institutional without social", "Institutional with social"),
values = c('green','red','blue')
)
#Screening completion rate: screen complete/screening referral (Means only)
#This shows us a big change. This demonstrates the STRENGTH of social navigation. (clearance rate?)
#This needs scrutiny. According to these rates we are only getting a 6% clearance per year?(!) (as measured by 0.050 * 12)
ggplot()+
theme_bw()+
geom_line(data = control.df_mean_at_time, aes(x=time, y=m_number.of.screen.completed/m_number.of.screening.referrals,color=con))+
#geom_line(data=control.df, alpha=0.1, aes(x=time, y=number.of.screen.completed/number.of.screening.referrals,color=con))+
geom_line(data = noSocial_intervention.df_mean_at_time, aes(x=time,y=m_number.of.screen.completed/m_number.of.screening.referrals,color=ins))+
#geom_line(data=noSocial_intervention.df, alpha=0.1, aes(x=time, y=number.of.screen.completed/number.of.dt.completed, color=ins))+
geom_line(data = intervention.df_mean_at_time, aes(x=time, y=m_number.of.screen.completed/m_number.of.screening.referrals, color=soc))+
#geom_line(data=intervention.df, alpha=0.1, aes(x=time, y=number.of.screen.completed/number.of.dt.completed, color=soc))+
scale_color_manual("Scenario", limits = c("control", "Institutional without social", "Institutional with social"),
values = c('green','red','blue')
)
#Screening completions/Screening Referrals (screening referral completion rate)
#This is showing the number of completions at t to total outstanding screening referral rate (clearance rate?)
#
ggplot()+
theme_bw()+
geom_line(data = control.df_mean_at_time, aes(x=time, y=m_number.of.screen.completed/m_number.of.screening.referrals,color=con))+
geom_line(data=control.df, alpha=0.1, aes(x=time, y=number.of.screen.completed/number.of.screening.referrals,color=con))+
geom_line(data = noSocial_intervention.df_mean_at_time, aes(x=time,y=m_number.of.screen.completed/m_number.of.screening.referrals,color=ins))+
geom_line(data=noSocial_intervention.df, alpha=0.1, aes(x=time, y=number.of.screen.completed/number.of.screening.referrals, color=ins))+
geom_line(data = intervention.df_mean_at_time, aes(x=time, y=m_number.of.screen.completed/m_number.of.screening.referrals, color=soc))+
geom_line(data=intervention.df, alpha=0.1, aes(x=time, y=number.of.screen.completed/number.of.screening.referrals, color=soc))+
scale_color_manual("Scenario", limits = c("control", "Institutional without social", "Institutional with social"),
values = c('green','red','blue')
)#+
#ylim(c(0,1))
#1 instance
ggplot()+
theme_bw()+
#geom_line(data = control.df_mean_at_time, aes(x=time, y=m_number.of.screen.completed/m_number.of.screening.referrals,color=con))+
geom_line(data=control.df[1:360,], aes(x=time, y=number.of.screen.completed/number.of.screening.referrals,color=con))+
#geom_line(data = noSocial_intervention.df_mean_at_time, aes(x=time,y=m_number.of.screen.completed/m_number.of.screening.referrals,color=ins))+
geom_line(data=noSocial_intervention.df[1:360,], aes(x=time, y=number.of.screen.completed/number.of.screening.referrals, color=ins))+
#geom_line(data = intervention.df_mean_at_time, aes(x=time, y=m_number.of.screen.completed/m_number.of.screening.referrals, color=soc))+
geom_line(data=intervention.df[1:360,], aes(x=time, y=number.of.screen.completed/number.of.screening.referrals, color=soc))+
scale_color_manual("Scenario", limits = c("control", "Institutional without social", "Institutional with social"),
values = c('green','red','blue'))
#2 instance
ggplot()+
theme_bw()+
#geom_line(data = control.df_mean_at_time, aes(x=time, y=m_number.of.screen.completed/m_number.of.screening.referrals,color=con))+
geom_line(data=control.df[360:720,], aes(x=time, y=number.of.screen.completed/number.of.screening.referrals,color=con))+
#geom_line(data = noSocial_intervention.df_mean_at_time, aes(x=time,y=m_number.of.screen.completed/m_number.of.screening.referrals,color=ins))+
geom_line(data=noSocial_intervention.df[360:720,], aes(x=time, y=number.of.screen.completed/number.of.screening.referrals, color=ins))+
#geom_line(data = intervention.df_mean_at_time, aes(x=time, y=m_number.of.screen.completed/m_number.of.screening.referrals, color=soc))+
geom_line(data=intervention.df[360:720,], aes(x=time, y=number.of.screen.completed/number.of.screening.referrals, color=soc))+
scale_color_manual("Scenario", limits = c("control", "Institutional without social", "Institutional with social"),
values = c('green','red','blue'))
#Diagnostic completions/diagnostic referrals (diagnostic referral completion rate)
ggplot()+
theme_bw()+
geom_line(data = control.df_mean_at_time, aes(x=time, y=m_number.of.dt.completed/m_number.of.diagnostic.referrals.at.t,color=con))+
geom_line(data=control.df, alpha=0.1, aes(x=time, y=number.of.dt.completed/number.of.diagnostic.referrals.at.t,color=con))+
geom_line(data = noSocial_intervention.df_mean_at_time, aes(x=time,y=m_number.of.dt.completed/m_number.of.diagnostic.referrals.at.t,color=ins))+
geom_line(data=noSocial_intervention.df, alpha=0.1, aes(x=time, y=number.of.dt.completed/number.of.diagnostic.referrals.at.t, color=ins))+
geom_line(data = intervention.df_mean_at_time, aes(x=time, y=m_number.of.dt.completed/m_number.of.diagnostic.referrals.at.t, color=soc))+
geom_line(data=intervention.df, alpha=0.1, aes(x=time, y=number.of.dt.completed/number.of.diagnostic.referrals.at.t, color=soc))+
scale_color_manual("Scenario", limits = c("control", "Institutional without social", "Institutional with social"),
values = c('green','red','blue')
)#+
# ylim(c(0,2))
ggplot()+
theme_bw()+
geom_line(data = control.df_mean_at_time, aes(x=time, y=m_number.of.dt.completed/m_number.of.diagnostic.referrals.at.t,color=con))+
#geom_line(data=control.df, alpha=0.1, aes(x=time, y=number.of.dt.completed/number.of.diagnostic.referrals.at.t,color=con))+
geom_line(data = noSocial_intervention.df_mean_at_time, aes(x=time,y=m_number.of.dt.completed/m_number.of.diagnostic.referrals.at.t,color=ins))+
#geom_line(data=noSocial_intervention.df, alpha=0.1, aes(x=time, y=number.of.dt.completed/number.of.diagnostic.referrals.at.t, color=ins))+
geom_line(data = intervention.df_mean_at_time, aes(x=time, y=m_number.of.dt.completed/m_number.of.diagnostic.referrals.at.t, color=soc))+
#geom_line(data=intervention.df, alpha=0.1, aes(x=time, y=number.of.dt.completed/number.of.diagnostic.referrals.at.t, color=soc))+
scale_color_manual("Scenario", limits = c("control", "Institutional without social", "Institutional with social"),
values = c('green','red','blue')
)+
ylim(c(0,2)) #ylim cuts off huge spike around t=1
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
#Agents diagnosed/Agents with cancer (Diagnosis rate)
ggplot()+
theme_bw()+
geom_line(data = control.df_mean_at_time, aes(x=time, y=m_number.of.diagnosed.cases/m_number.of.positive.bc.agents,color=con))+
geom_line(data=control.df, alpha=0.1, aes(x=time, y=number.of.diagnosed.cases/number.of.positive.bc.agents,color=con))+
geom_line(data = noSocial_intervention.df_mean_at_time, aes(x=time,y=m_number.of.diagnosed.cases/m_number.of.positive.bc.agents,color=ins))+
geom_line(data=noSocial_intervention.df, alpha=0.1, aes(x=time, y=number.of.diagnosed.cases/number.of.positive.bc.agents, color=ins))+
geom_line(data = intervention.df_mean_at_time, aes(x=time, y=m_number.of.diagnosed.cases/m_number.of.positive.bc.agents, color=soc))+
geom_line(data=intervention.df, alpha=0.1, aes(x=time, y=number.of.diagnosed.cases/number.of.positive.bc.agents, color=soc))+
scale_color_manual("Scenario", limits = c("control", "Institutional without social", "Institutional with social"),
values = c('green','red','blue')
)
mean(control.df_mean_at_time$m_number.of.diagnosed.cases/control.df_mean_at_time$m_number.of.positive.bc.agents)
## [1] 0.4706628
mean(noSocial_intervention.df_mean_at_time$m_number.of.diagnosed.cases/noSocial_intervention.df_mean_at_time$m_number.of.positive.bc.agents)
## [1] 0.4844583
mean(intervention.df_mean_at_time$m_number.of.diagnosed.cases/intervention.df_mean_at_time$m_number.of.positive.bc.agents)
## [1] 0.4993559
#Stage at diagnosis (Early = 0-1, late = 2-3) (navigated vs unnavigated?)
#Early
ggplot()+
theme_bw()+
geom_line(data = control.df_mean_at_time, aes(x=time, y=m_early.diagnosed.ratio,color=con))+
geom_line(data=control.df, alpha=0.1, aes(x=time, y=(number.of.ss0.diagnosed+number.of.ss1.diagnosed)/number.of.diagnosed.cases,color=con))+
geom_line(data = noSocial_intervention.df_mean_at_time, aes(x=time,y=m_early.diagnosed.ratio,color=ins))+
geom_line(data=noSocial_intervention.df, alpha=0.1, aes(x=time, y=(number.of.ss0.diagnosed+number.of.ss1.diagnosed)/number.of.diagnosed.cases, color=ins))+
geom_line(data = intervention.df_mean_at_time, aes(x=time, y=m_early.diagnosed.ratio, color=soc))+
geom_line(data=intervention.df, alpha=0.1, aes(x=time, y=(number.of.ss0.diagnosed+number.of.ss1.diagnosed)/number.of.diagnosed.cases, color=soc))+
scale_color_manual("Scenario", values = c('green','red','blue'),
limits= c("control", "Institutional without social", "Institutional with social")
)+
ylim(c(0,1))
#Late
ggplot()+
theme_bw()+
geom_line(data = control.df_mean_at_time, aes(x=time, y=m_late.diagnosed.ratio,color=con))+
geom_line(data=control.df, alpha=0.1, aes(x=time, y=(number.of.ss2.diagnosed+number.of.ss3.diagnosed)/number.of.diagnosed.cases,color=con))+
geom_line(data = noSocial_intervention.df_mean_at_time, aes(x=time,y=m_late.diagnosed.ratio,color=ins))+
geom_line(data=noSocial_intervention.df, alpha=0.1, aes(x=time, y=(number.of.ss2.diagnosed+number.of.ss3.diagnosed)/number.of.diagnosed.cases, color=ins))+
geom_line(data = intervention.df_mean_at_time, aes(x=time, y=m_late.diagnosed.ratio, color=soc))+
geom_line(data=intervention.df, alpha=0.1, aes(x=time, y=(number.of.ss2.diagnosed+number.of.ss3.diagnosed)/number.of.diagnosed.cases, color=soc))+
scale_color_manual("Scenario", values = c('green','red','blue'),
limits= c("control", "Institutional without social", "Institutional with social")
)+
ylim(c(0,1))
#Effects ~3% between control and social (for feb 16 meeting)
mean(control.df_mean_at_time$m_late.diagnosed.ratio)
## [1] 0.8224206
#0.8906187
mean(noSocial_intervention.df_mean_at_time$m_late.diagnosed.ratio)
## [1] 0.8100797
#0.8790043
mean(intervention.df_mean_at_time$m_late.diagnosed.ratio)
## [1] 0.7979164
#0.8597492
#Proportion of agents diagnosed within 60 day "golden period" (compare navigated and non-navigated)
#NOTE: this depends on time.until.diagnosis being correct
#As of Feb 16, 2021 we see that time.until.diagnosis ranges from 17-30, with an avg of ~23
ggplot()+
theme_bw()+
#geom_line(data = control.df_mean_at_time, aes(x=time, y=length(which(m_time.until.diagnosis <= 2)),color=con))+
geom_line(data=control.df, alpha=0.1, aes(x=time, y=length(which(time.until.diagnosis <= 2)),color=con))+
#geom_line(data = noSocial_intervention.df_mean_at_time, aes(x=time,y=length(which(m_time.until.diagnosis <= 2)),color=ins))+
geom_line(data=noSocial_intervention.df, alpha=0.1, aes(x=time, y=length(which(time.until.diagnosis <= 2)), color=ins))+
#geom_line(data = intervention.df_mean_at_time, aes(x=time, y=length(which(m_time.until.diagnosis <= 2)), color=soc))+
geom_line(data=intervention.df, alpha=0.1, aes(x=time, y=length(which(time.until.diagnosis <= 2)), color=soc))+
scale_color_manual("Scenario", values = c('green','red','blue'),
limits= c("control", "Institutional without social", "Institutional with social")
)
#ylim(c(0,1))
#NOTE (feb 17 meeting):
#NO agents are diagnosed in less than 60 days
#> length(which(control.df$time.until.diagnosis <= 2))
#[1] 0
#> length(which(intervention.df$time.until.diagnosis <= 2))
#[1] 0
#> length(which(noSocial_intervention.df$time.until.diagnosis <= 2))
#[1] 0
#In fact, the fastest diagnosis takes 16 months...
#> min(noSocial_intervention.df$time.until.diagnosis)
#[1] 16
#TODO
#Referral to diagnosis time
#Time to diagnosis
ggplot()+
theme_bw()+
geom_line(data = control.df_mean_at_time, aes(x=time, y=m_time.until.diagnosis,color=con))+
geom_line(data=control.df, alpha=0.1, aes(x=time, y=time.until.diagnosis,color=con))+
geom_line(data = noSocial_intervention.df_mean_at_time, aes(x=time,y=m_time.until.diagnosis,color=ins))+
geom_line(data=noSocial_intervention.df, alpha=0.1, aes(x=time, y=time.until.diagnosis, color=ins))+
geom_line(data = intervention.df_mean_at_time, aes(x=time, y=m_time.until.diagnosis, color=soc))+
geom_line(data=intervention.df, alpha=0.1, aes(x=time, y=time.until.diagnosis, color=soc))+
scale_color_manual("Scenario", values = c('green','red','blue'),
limits= c("control", "Institutional without social", "Institutional with social")
)+
ylim(c(0,30))
#Total number of navigated
ggplot()+
theme_bw()+
geom_line(data = control.df_mean_at_time, aes(x=time, y=m_number.of.navigated.agents,color=con))+
geom_line(data=control.df, alpha=0.1, aes(x=time, y=number.of.navigated.agents, color=con))+
geom_line(data = noSocial_intervention.df_mean_at_time, aes(x=time,y=m_number.of.navigated.agents,color=ins))+
geom_line(data=noSocial_intervention.df, alpha=0.1, aes(x=time, y=number.of.navigated.agents,color=ins))+
geom_line(data = intervention.df_mean_at_time, aes(x=time, y=m_number.of.navigated.agents, color=soc))+
geom_line(data=intervention.df, alpha=0.1, aes(x=time, y=number.of.navigated.agents, color=soc))+
scale_color_manual("Scenario", values = c('green','red','blue'),
limits = c(con, ins, soc)
)+
ylim(c(0,300))
#number.of.positive.bc.agents
ggplot()+
theme_bw()+
geom_line(data = control.df_mean_at_time, aes(x=time, y=m_number.of.positive.bc.agents,color="control"))+
geom_line(data=control.df, alpha=0.1, aes(x=time, y=number.of.positive.bc.agents,color=con))+
geom_line(data = noSocial_intervention.df_mean_at_time, aes(x=time, y=m_number.of.positive.bc.agents,color="Institutional without social"))+
geom_line(data=noSocial_intervention.df,alpha=0.1, aes(x=time, y=number.of.positive.bc.agents,color=ins))+
geom_line(data = intervention.df_mean_at_time, aes(x=time, y=m_number.of.positive.bc.agents, color="Institutional with social"))+
geom_line(data=intervention.df, alpha=0.1, aes(x=time, y=number.of.positive.bc.agents, color=soc))+
scale_color_manual("Scenario", values = c('green','red','blue'),
limits = c("control", "Institutional without social", "Institutional with social")
)+
ylim(c(50, 140))
#(number.of.positive.bc.agents/N)*100
#Breast Cancer Prevalence (%)
ggplot()+
theme_bw()+
geom_line(data=control.df, alpha=0.1,aes(x=time, y=((number.of.positive.bc.agents/N)*100),color=con))+
geom_line(data=control.df_mean_at_time, aes(x=time, y=((m_number.of.positive.bc.agents/N)*100),color=con))+
geom_line(data=noSocial_intervention.df, alpha=0.1,aes(x=time, y=((number.of.positive.bc.agents/N)*100),color=ins))+
geom_line(data=noSocial_intervention.df_mean_at_time, aes(x=time, y=((m_number.of.positive.bc.agents/N)*100),color=ins))+
geom_line(data=intervention.df, alpha=0.1,aes(x=time, y=((number.of.positive.bc.agents/N)*100), color=soc))+
geom_line(data=intervention.df_mean_at_time, aes(x=time, y=((m_number.of.positive.bc.agents/N)*100), color=soc))+
labs(y="Breast Cancer Prevalence (%)")+
scale_color_manual("Scenario", values = c('green','red','blue'),
limits= c("control", "Institutional without social", "Institutional with social")
)+
ylim(c(1,3))
#number.of.screening.visits.at.t
ggplot()+
theme_bw()+
geom_line(data=control.df, alpha=0.1, aes(x=time, y=number.of.screening.visits.at.t,color=con))+
geom_line(data=control.df_mean_at_time, aes(x=time, y=m_number.of.screening.visits.at.t,color=con))+
geom_line(data=noSocial_intervention.df, alpha=0.1, aes(x=time, y=number.of.screening.visits.at.t,color=ins))+
geom_line(data=noSocial_intervention.df_mean_at_time, aes(x=time, y=m_number.of.screening.visits.at.t,color=ins))+
geom_line(data=intervention.df, alpha=0.1, aes(x=time, y=number.of.screening.visits.at.t, color=soc))+
geom_line(data=intervention.df_mean_at_time, aes(x=time, y=m_number.of.screening.visits.at.t, color=soc))+
scale_color_manual("Scenario", values = c('green','red','blue'),
limits = c("control", "Institutional without social", "Institutional with social")
)+
ylim(c(0,125))
#number.of.diagnostic.referrals.at.t
ggplot()+
theme_bw()+
geom_line(data=control.df, alpha=0.1, aes(x=time, y=number.of.diagnostic.referrals.at.t,color=con))+
geom_line(data=control.df_mean_at_time, aes(x=time, y=m_number.of.diagnostic.referrals.at.t,color=con))+
geom_line(data=noSocial_intervention.df, alpha=0.1, aes(x=time, y=number.of.diagnostic.referrals.at.t,color=ins))+
geom_line(data=noSocial_intervention.df_mean_at_time, aes(x=time, y=m_number.of.diagnostic.referrals.at.t,color=ins))+
geom_line(data=intervention.df, alpha=0.1, aes(x=time, y=number.of.diagnostic.referrals.at.t, color=soc))+
geom_line(data=intervention.df_mean_at_time, aes(x=time, y=m_number.of.diagnostic.referrals.at.t, color=soc))+
scale_color_manual("Scenario", values = c('green','red','blue'),
limits = c("control", "Institutional without social", "Institutional with social")
)+
ylim(c(0,20))
#number.of.screening.referrals
ggplot()+
theme_bw()+
geom_line(data=control.df, alpha=0.1, aes(x=time, y=number.of.screening.referrals,color=con))+
geom_line(data=control.df_mean_at_time, aes(x=time, y=m_number.of.screening.referrals,color=con))+
geom_line(data=noSocial_intervention.df, alpha=0.1, aes(x=time, y=number.of.screening.referrals,color=ins))+
geom_line(data=noSocial_intervention.df_mean_at_time, aes(x=time, y=m_number.of.screening.referrals,color=ins))+
geom_line(data=intervention.df, alpha=0.1, aes(x=time, y=number.of.screening.referrals, color=soc))+
geom_line(data=intervention.df_mean_at_time, aes(x=time, y=m_number.of.screening.referrals, color=soc))+
scale_color_manual("Scenario", values = c('green','red','blue'),
limits = c("control", "Institutional without social", "Institutional with social")
)
#ylim(c(700,1300)) #omits data that does not conform
#number.of.diagnostic.referrals.at.t/number.of.screening.visits.at.t
ggplot()+
theme_bw()+
geom_line(data=control.df, alpha=0.1, aes(x=time, y=number.of.diagnostic.referrals.at.t/number.of.screening.visits.at.t, color = con))+
geom_line(data=control.df_mean_at_time, aes(x=time, y=m_number.of.diagnostic.referrals.at.t/m_number.of.screening.visits.at.t, color = con))+
geom_line(data=noSocial_intervention.df,alpha=0.1, aes(x=time, y=number.of.diagnostic.referrals.at.t/number.of.screening.visits.at.t,color=ins))+
geom_line(data=noSocial_intervention.df_mean_at_time, aes(x=time, y=m_number.of.diagnostic.referrals.at.t/m_number.of.screening.visits.at.t,color=ins))+
geom_line(data=intervention.df, alpha=0.1, aes(x=time, y=number.of.diagnostic.referrals.at.t/number.of.screening.visits.at.t, color=soc))+
geom_line(data=intervention.df_mean_at_time, aes(x=time, y=m_number.of.diagnostic.referrals.at.t/m_number.of.screening.visits.at.t, color=soc))+
scale_color_manual("Scenario", values = c('green','red','blue'),
limits = c("control", "Institutional without social", "Institutional with social")
)+
ylim(c(0,0.5))
#number.of.dt.completed/number.of.screen.completed
ggplot()+
theme_bw()+
geom_line(data=control.df, alpha=0.1, aes(x=time, y=number.of.dt.completed/number.of.screen.completed,color=con))+
geom_line(data=control.df_mean_at_time, aes(x=time, y=m_number.of.dt.completed/m_number.of.screen.completed,color=con))+
geom_line(data=noSocial_intervention.df, alpha=0.1, aes(x=time, y=number.of.dt.completed/number.of.screen.completed,color=ins))+
geom_line(data=noSocial_intervention.df_mean_at_time, aes(x=time, y=m_number.of.dt.completed/m_number.of.screen.completed,color=ins))+
geom_line(data=intervention.df, alpha=0.1, aes(x=time, y=number.of.dt.completed/number.of.screen.completed, color=soc))+
geom_line(data=intervention.df_mean_at_time, aes(x=time, y=m_number.of.dt.completed/m_number.of.screen.completed, color=soc))+
scale_color_manual("Scenario", values = c('green','red','blue'),
limits = c("control", "Institutional without social", "Institutional with social")
)+
ylim(c(0,0.2))
## Warning: Removed 60 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 60 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 60 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
#number.of.screen.completed
ggplot()+
theme_bw()+
geom_line(data=control.df, alpha=0.1, aes(x=time, y=number.of.screen.completed,color=con))+
geom_line(data=control.df_mean_at_time, aes(x=time, y=m_number.of.screen.completed,color=con))+
geom_line(data=noSocial_intervention.df, alpha=0.1, aes(x=time, y=number.of.screen.completed,color=ins))+
geom_line(data=noSocial_intervention.df_mean_at_time, aes(x=time, y=m_number.of.screen.completed,color=ins))+
geom_line(data=intervention.df, alpha=0.1, aes(x=time, y=number.of.screen.completed, color=soc))+
geom_line(data=intervention.df_mean_at_time, aes(x=time, y=m_number.of.screen.completed, color=soc))+
scale_color_manual("Scenario", values = c('green','red','blue'),
limits = c("control", "Institutional without social", "Institutional with social")
)+
ylim(c(25,125))
#number.of.dt.completed
ggplot()+
theme_bw()+
geom_line(data=control.df, alpha=0.1, aes(x=time, y=number.of.dt.completed,color=con))+
geom_line(data=control.df_mean_at_time, aes(x=time, y=m_number.of.dt.completed,color=con))+
geom_line(data=noSocial_intervention.df, alpha=0.1, aes(x=time, y=number.of.dt.completed,color=ins))+
geom_line(data=noSocial_intervention.df_mean_at_time, aes(x=time, y=m_number.of.dt.completed,color=ins))+
geom_line(data=intervention.df, alpha=0.1, aes(x=time, y=number.of.dt.completed, color=soc))+
geom_line(data=intervention.df_mean_at_time, aes(x=time, y=m_number.of.dt.completed, color=soc))+
scale_color_manual("Scenario", values = c('green','red','blue'),
limits = c("control", "Institutional without social", "Institutional with social")
)+
ylim(c(0,15))
## Warning: Removed 60 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 60 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
## Warning: Removed 60 rows containing missing values (geom_path).
## Warning: Removed 2 rows containing missing values (geom_path).
##Skipped for now
ggplot(control.df, aes(x=time))+
geom_line(aes(y=number.of.hpos.agents), alpha=0.1)+
geom_line(aes(y=number.of.hneg.agents), alpha=0.1)+
geom_line(aes(y=number.of.positive.bc.agents), alpha=0.1)+
theme_bw()+
geom_line(data = control.df_mean_at_time, aes(x=time, y=m_number.of.hpos.agents))+
geom_line(data = control.df_mean_at_time, aes(x=time, y=m_number.of.hneg.agents))+
geom_line(data = control.df_mean_at_time, aes(x=time, y=m_number.of.positive.bc.agents))+
ylim(c(0, 150))+
annotate(geom="text", x=625, y=112, label="All cases", col="black")+
annotate(geom="text", x=625, y=90, label="Hormone-positive", col="black")+
annotate(geom="text", x=625, y=30, label="Hormone-negative", col="black")+
labs(y="number")
#number.of.hneg.agents/number.of.hpos.agents
ggplot()+
theme_bw()+
geom_line(data=control.df, alpha=0.1, aes(x=time, y=number.of.hneg.agents/number.of.hpos.agents,color=con))+
geom_line(data=control.df_mean_at_time, aes(x=time, y=m_number.of.hneg.agents/m_number.of.hpos.agents,color=con))+
geom_line(data=noSocial_intervention.df, alpha=0.1, aes(x=time, y=number.of.hneg.agents/number.of.hpos.agents,color=ins))+
geom_line(data=noSocial_intervention.df_mean_at_time, aes(x=time, y=m_number.of.hneg.agents/m_number.of.hpos.agents,color=ins))+
geom_line(data=intervention.df, alpha=0.1, aes(x=time, y=number.of.hneg.agents/number.of.hpos.agents, color=soc))+
geom_line(data=intervention.df_mean_at_time, aes(x=time, y=m_number.of.hneg.agents/m_number.of.hpos.agents, color=soc))+
scale_color_manual("Scenario", values = c('green','red','blue'),
limits = c("control", "Institutional without social", "Institutional with social")
)+
ylim(c(0,1))
#number.of.diagnosed.cases
ggplot()+
theme_bw()+
geom_line(data=control.df, alpha=0.1, aes(x=time, y=number.of.diagnosed.cases,color=con))+
geom_line(data=control.df_mean_at_time, aes(x=time, y=m_number.of.diagnosed.cases,color=con))+
geom_line(data=noSocial_intervention.df,alpha=0.1, aes(x=time, y=number.of.diagnosed.cases,color=ins))+
geom_line(data=noSocial_intervention.df_mean_at_time, aes(x=time, y=m_number.of.diagnosed.cases,color=ins))+
geom_line(data=intervention.df, alpha=0.1, aes(x=time, y=number.of.diagnosed.cases, color=soc))+
geom_line(data=intervention.df_mean_at_time, aes(x=time, y=m_number.of.diagnosed.cases, color=soc))+
scale_color_manual("Scenario", values = c('green','red','blue'),
limits = c("control", "Institutional without social", "Institutional with social")
)+
ylim(c(20,60))
#number.of.positive.bc.agents
ggplot()+
theme_bw()+
geom_line(data=control.df, alpha=0.1, aes(x=time, y=number.of.positive.bc.agents,color=con))+
geom_line(data=control.df_mean_at_time, aes(x=time, y=m_number.of.positive.bc.agents,color=con))+
geom_line(data=noSocial_intervention.df, alpha=0.1, aes(x=time, y=number.of.positive.bc.agents,color=ins))+
geom_line(data=noSocial_intervention.df_mean_at_time, aes(x=time, y=m_number.of.positive.bc.agents,color=ins))+
geom_line(data=intervention.df,alpha=0.1, aes(x=time, y=number.of.positive.bc.agents, color=soc))+
geom_line(data=intervention.df_mean_at_time, aes(x=time, y=m_number.of.positive.bc.agents, color=soc))+
scale_color_manual("Scenario", values = c('green','red','blue'),
limits = c("control", "Institutional without social", "Institutional with social")
)+
ylim(c(70,140))
#nintros
ggplot()+
theme_bw()+
geom_line(data=control.df,alpha=0.1, aes(x=time, y=nintros,color=con))+
geom_line(data=control.df_mean_at_time, aes(x=time, y=m_nintros,color=con))+
geom_line(data=noSocial_intervention.df, alpha=0.1, aes(x=time, y=nintros,color=ins))+
geom_line(data=noSocial_intervention.df_mean_at_time,aes(x=time, y=m_nintros,color=ins))+
geom_line(data=intervention.df, alpha=0.1, aes(x=time, y=nintros, color=soc))+
geom_line(data=intervention.df_mean_at_time, aes(x=time, y=m_nintros, color=soc))+
scale_color_manual("Scenario", values = c('green','red','blue'),
limits = c("control", "Institutional without social", "Institutional with social")
)+
ylim(c(40,110))
#}
#end capturing plots if not using knit
#dev.off()